SPARKS is a fully open source, distributed in-memory machine learning platform with linear scalability. SPARKS supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. SPARK employs the techniques of data scientists in an easy to use application that helps scale your data science efforts using automation and state-of-the-art computing power to accomplish tasks in minutes that used to take months.
Algorithms developed from the ground up for distributed computing and for both supervised and unsupervised approaches including Random Forest, GLM, GBM, XGBoost, GLRM, Word2Vec and many more.
Use the programing language you already know like R, Python and others to build models in your dashboard, or use our own graphical notebook based interactive user interface that only requires minimal coding.
SPARKS employs a host of different techniques and methodologies for interpreting and explaining the results of its models.
In-memory processing with fast serialization between nodes and clusters to support massive datasets. Distributed processing on big data delivers speeds up to 50x faster with fine-grain parallelism, enabling optimal efficiency without introducing degradation in computational accuracy.
Easy to deploy POC to deploy models for fast and accurate scoring in any environment, including very large models.
SPARKS automatically generates visualizations and creates data plots that are most relevant from a statistical perspective based on the most relevant data statistics to help users get a quick understanding of their data prior to starting the model building process.
SPARKS works on existing big data infrastructure, on bare metal or on top of existing Hadoop or Spark clusters. It can ingest data directly from HDFS, Spark, S3, Azure Data Lake or any other data source into it’s in-memory distributed key value store.
SPARKS takes advantage of the computing power of distributed systems and in-memory computing to accelerate machine learning using it’s industry parallelized algorithms which take advantage of fine grained in-memory mapreduce
Use best practice model recipes and the power of high performance computing to Iterate across thousands of possible models including advanced feature engineering, model tuning and model stacking (coming soon).
Smart Data is becoming more useful to corporations each day. However, numerous companies are unaware of the procedures or cost on how to process Big Data into Smart Data that would be beneficial. You have your own data that needs "translation", we'll do it for you.Schedule Consultation
Big Data is commonly described as using the five Vs: value, variety, volume, velocity, veracity. A reduction in “volume” takes place with Smart Data. Only useful information for solving the problem is presented. Variety may, or may not, be reduced, depending on the screening process used in filtering the data. Value, velocity, and veracity (accuracy) should all increase with the decrease in volume.
Machine Learning is often a training process for Artificial Intelligence platforms, but can also be used as a recognition and decision-making program. As the use and popularity of Smart Data has increased, it has also been used with Machine Learning algorithms designed to seek out Business Intelligence and insights. Machine Learning allows organizations to filter Data Lakes and Data Warehouses, creating Smart Data in the process.
During the screening and filtering process of creating Smart Data, decisions are made as to which data should be blocked, and which should be presented. Machine Learning and Artificial Intelligence (AI) use specific criteria during this process. AI is an ongoing attempt to create intelligence within machines, allowing them to work and respond like humans. Artificial Intelligence has provided flexibility and can address unique goals. For example, financial services firms can use AI-driven Smart Data for customer analysis, fraud detection, market analysis, and compliance.